Best Data Pipeline Software for Apache Spark
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Compare the Top Data Pipeline Software that integrates with Apache Spark as of December 2025
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This a list of Data Pipeline software that integrates with Apache Spark. Use the filters on the left to add additional filters for products that have integrations with Apache Spark. View the products that work with Apache Spark in the table below.
What is Data Pipeline Software for Apache Spark?
Data pipeline software helps businesses automate the movement, transformation, and storage of data from various sources to destinations such as data warehouses, lakes, or analytic platforms. These platforms provide tools for extracting data from multiple sources, processing it in real-time or batch, and loading it into target systems for analysis or reporting (ETL: Extract, Transform, Load). Data pipeline software often includes features for data monitoring, error handling, scheduling, and integration with other software tools, making it easier for organizations to ensure data consistency, accuracy, and flow. By using this software, businesses can streamline data workflows, improve decision-making, and ensure that data is readily available for analysis. Compare and read user reviews of the best Data Pipeline software for Apache Spark currently available using the table below. This list is updated regularly.
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1
Dagster
Dagster Labs
Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.Starting Price: $0 -
2
Yandex Data Proc
Yandex
You select the size of the cluster, node capacity, and a set of services, and Yandex Data Proc automatically creates and configures Spark and Hadoop clusters and other components. Collaborate by using Zeppelin notebooks and other web apps via a UI proxy. You get full control of your cluster with root permissions for each VM. Install your own applications and libraries on running clusters without having to restart them. Yandex Data Proc uses instance groups to automatically increase or decrease computing resources of compute subclusters based on CPU usage indicators. Data Proc allows you to create managed Hive clusters, which can reduce the probability of failures and losses caused by metadata unavailability. Save time on building ETL pipelines and pipelines for training and developing models, as well as describing other iterative tasks. The Data Proc operator is already built into Apache Airflow.Starting Price: $0.19 per hour -
3
Lyftrondata
Lyftrondata
Whether you want to build a governed delta lake, data warehouse, or simply want to migrate from your traditional database to a modern cloud data warehouse, do it all with Lyftrondata. Simply create and manage all of your data workloads on one platform by automatically building your pipeline and warehouse. Analyze it instantly with ANSI SQL, BI/ML tools, and share it without worrying about writing any custom code. Boost the productivity of your data professionals and shorten your time to value. Define, categorize, and find all data sets in one place. Share these data sets with other experts with zero codings and drive data-driven insights. This data sharing ability is perfect for companies that want to store their data once, share it with other experts, and use it multiple times, now and in the future. Define dataset, apply SQL transformations or simply migrate your SQL data processing logic to any cloud data warehouse. -
4
Astro by Astronomer
Astronomer
For data teams looking to increase the availability of trusted data, Astronomer provides Astro, a modern data orchestration platform, powered by Apache Airflow, that enables the entire data team to build, run, and observe data pipelines-as-code. Astronomer is the commercial developer of Airflow, the de facto standard for expressing data flows as code, used by hundreds of thousands of teams across the world. -
5
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
6
Azure Data Factory
Microsoft
Integrate data silos with Azure Data Factory, a service built for all data integration needs and skill levels. Easily construct ETL and ELT processes code-free within the intuitive visual environment, or write your own code. Visually integrate data sources using more than 90+ natively built and maintenance-free connectors at no added cost. Focus on your data—the serverless integration service does the rest. Data Factory provides a data integration and transformation layer that works across your digital transformation initiatives. Data Factory can help independent software vendors (ISVs) enrich their SaaS apps with integrated hybrid data as to deliver data-driven user experiences. Pre-built connectors and integration at scale enable you to focus on your users while Data Factory takes care of the rest. -
7
Kestra
Kestra
Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. The UI automatically adjusts the YAML definition any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is defined declaratively in code, even if some workflow components are modified in other ways. -
8
definity
definity
Monitor and control everything your data pipelines do with zero code changes. Monitor data and pipelines in motion to proactively prevent downtime and quickly root cause issues. Optimize pipeline runs and job performance to save costs and keep SLAs. Accelerate code deployments and platform upgrades while maintaining reliability and performance. Data & performance checks in line with pipeline runs. Checks on input data, before pipelines even run. Automatic preemption of runs. definity takes away the effort to build deep end-to-end coverage, so you are protected at every step, across every dimension. definity shifts observability to post-production to achieve ubiquity, increase coverage, and reduce manual effort. definity agents automatically run with every pipeline, with zero footprints. Unified view of data, pipelines, infra, lineage, and code for every data asset. Detect in run-time and avoid async checks. Auto-preempt runs, even on inputs. -
9
Unravel
Unravel Data
Unravel makes data work anywhere: on Azure, AWS, GCP or in your own data center– Optimizing performance, automating troubleshooting and keeping costs in check. Unravel helps you monitor, manage, and improve your data pipelines in the cloud and on-premises – to drive more reliable performance in the applications that power your business. Get a unified view of your entire data stack. Unravel collects performance data from every platform, system, and application on any cloud then uses agentless technologies and machine learning to model your data pipelines from end to end. Explore, correlate, and analyze everything in your modern data and cloud environment. Unravel’s data model reveals dependencies, issues, and opportunities, how apps and resources are being used, what’s working and what’s not. Don’t just monitor performance – quickly troubleshoot and rapidly remediate issues. Leverage AI-powered recommendations to automate performance improvements, lower costs, and prepare.
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